CLOUD COVER AND INTERPLANETARY MAGNETIC FIELD: POSSIBLE RELATIONSHIP

Solar energy is the main driver of the climate on Eart, thus the variation of solar activity may affect climate variability via changes in irradiation, energetic particles, cosmic ray flux or solar wind parameters. Solar wind is characterized by speed, magnetic and electric fields, flow pressure, particle flux, dynamic pressure, with various effects on atmospheric processes. One of these is the formation and evolution of clouds which play a crucial role in the terrestrial climate, since they induce cooling or warming effects, depending on their heights and composition. Possible relationship between solar activity and cloud cover variability are lately the subject of various studies, but no clear conclusion exists due to contradictory results obtained so far. This article studies the possible relationship between mean cloud cover and the interplanetary magnetic field at global scale, as well as geographical/regional characteristics for the 1984 – 2009 period, i. e. for solar cycles 22-23, when satellite observations are available at global scale and on a continuous basis. The study also shows the seasonal dependence and is made for different cloud height and composition, i. e. for low/middle/high and liquid/ice types of clouds

Early-onset of Atlantic Meridional Overturning Circulation weakening in response to atmospheric CO2 concentration

The Atlantic Meridional Overturning Circulation (AMOC), a tipping component of the climate system, is projected to slowdown during the 21st century in response to increased atmospheric CO2 concentration. The rate and start of the weakening are associated with relatively large uncertainties. Observed sea surface temperature-based reconstructions indicate that AMOC has been weakening since the mid-20th century, but its forcing factors are not fully understood. Here we provide dynamical observational evidence that the increasing atmospheric CO2 concentration affects the North Atlantic heat fluxes and precipitation rate, and weakens AMOC, consistent with numerical simulations. The inferred weakening, starting in the late 19th century, earlier than previously suggested, is estimated at 3.7 ± 1.0 Sv over the 1854–2016 period, which is larger than it is shown in numerical simulations (1.4 ± 1.4 Sv)

Estimating Annual CO2 Flux for Lutjewad Station Using Three Different Gap-Filling Techniques

Long-term measurements of CO2 flux can be obtained using the eddy covariance technique, but these datasets are affected by gaps which hinder the estimation of robust long-term means and annual ecosystem exchanges. We compare results obtained using three gap-fill techniques: multiple regression (MR), multiple imputation (MI), and artificial neural networks (ANNs), applied to a one-year dataset of hourly CO2 flux measurements collected in Lutjewad, over a flat agriculture area near the Wadden Sea dike in the north of the Netherlands. The dataset was separated in two subsets: a learning and a validation set. The performances of gap-filling techniques were analysed by calculating statistical criteria: coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), maximum absolute error (MaxAE), and mean square bias (MSB). The gap-fill accuracy is seasonally dependent, with better results in cold seasons. The highest accuracy is obtained using ANN technique which is also less sensitive to environmental/seasonal conditions. We argue that filling gaps directly on measured CO2 fluxes is more advantageous than the common method of filling gaps on calculated net ecosystem change, because ANN is an empirical method and smaller scatter is expected when gap filling is applied directly to measurements

The 3D model of the plasmasphere coupled to the ionosphere

Pierrard and Stegen (2008) has been coupled with the ionospheric IRI model. In addition to the electron number density, the plasmaspheric model is also developed to include the temperature profiles of the different particles and ion composition at altitudes from 60 to 2000 km. Results of the model for the F region trough are compared with coincident observations of middle and top ionosphere by means of satellite tomography and radar measurements. A good match between the model and observations supports the idea that the present model is useful for investigating physical mechanism involved in the plasmasphere‐ionosphere coupling and for acquiring information about the plasmaspheric behaviour based on ionospheric observation

Clouds and the Near-Earth Environment: Possible Links

Climate variability is a hot topic not only for scientists and policy-makers, but also for each and every one of us. The anthropogenic activities are considered to be responsible for most climate change, however there are large uncertainties about the magnitude of effects of solar variability and other extraterrestrial influences, such as galactic cosmic rays on terrestrial climate. Clouds play an important role due to feedbacks of the radiation budget: variation of cloud cover/composition affects climate, which, in turn, affects cloud cover via atmospheric dynamics and sea temperature variations. Cloud formation and evolution are still under scientific scrutiny, since their microphysics is still not understood. Besides atmospheric dynamics and other internal climatic parameters, extraterrestrial sources of cloud cover variation are considered. One of these is the solar wind, whose effect on cloud cover might be modulated by the global atmospheric electrical circuit. Clouds height and composition, their seasonal variation and latitudinal distribution should be considered when trying to identify possible mechanisms by which solar energy is transferred to clouds. The influence of the solar wind on cloud formation can be assessed also through the ap index - the geomagnetic storm index, which can be readily connected with interplanetary magnetic field, IMF structure. This paper proposes to assess the possible relationship between both cloud cover and solar wind proxies, as the ap index, function of cloud height and composition and also through seasonal studies. The data covers almost three solar cycles (1984-2009). Mechanisms are looked for by investigating observed trends or correlation at local/seasonal scal

Satellite Observations of NO 2

Satellite-based measurements of atmospheric trace gases loading give a realistic image of atmospheric pollution at global, regional, and urban level. The aim of this paper is to investigate the trend of atmospheric NO2 content over Romania for the period 1996–2010 for several regions which are generally characterized by different pollutant loadings, resulting from GOME-1, SCIAMACHY, OMI, and GOME-2 instruments. Satellite results are then compared with ground-based in situ measurements made in industrial and relatively clean areas of one major city in Romania. This twofold approach will help in estimating whether the trend of NO2 obtained by means of data satellite retrievals can be connected with the evolution of national industry and transportation

Deconstructing Global Observed and Reanalysis Total Cloud Cover Fields Based on Pacific Climate Modes

Clouds are notoriously difficult to simulate. Here, we separate and quantify the impact of Pacific climate modes on total cloud cover (TCC) variability, using reliable satellite observations together with state-of-the art reanalysis outputs, over the 1979–2020 period. The two most prominent modes of annual TCC variability show intense loadings over the Pacific basin and explain most of the variance in what could be considered the “signal” in satellite TCC data. Canonical correlation analysis (CCA) provides coupled TCC—sea surface temperature (SST) patterns that are linked to the Eastern Pacific (EP) ElNiño—Southern Oscillation (ENSO) and the Central Pacific (CP) ENSO in a physically consistent manner. The two ENSO modes dominate global coupled SST–TCC variability with the footprint of the CP ENSO explaining roughly half of the variance induced by the EP ENSO among these coupled fields. Both the EP and the CP ENSO exert an influence on Pacific decadal TCC variability. The impact of both ENSO modes on global total cloud cover variability is amplified by two positive feedbacks. These results could be used as a reference for model investigations on future projections of coupled TCC—SST variability responses to the CP and the EP ENSO

OMI and Ground-Based In-Situ Tropospheric Nitrogen Dioxide Observations over Several Important European Cities during 2005–2014

In this work we present the evolution of tropospheric nitrogen dioxide (NO2) content over several important European cities during 2005–2014 using space observations and ground-based in-situ measurements. The NO2 content was derived using the daily observations provided by the Ozone Monitoring Instrument (OMI), while the NO2 volume mixing ratio measurements were obtained from the European Environment Agency (EEA) air quality monitoring stations database. The European cities selected are: Athens (37.98° N, 23.72° E), Berlin (52.51° N, 13.41° E), Bucharest (44.43° N, 26.10° E), Madrid (40.38° N, 3.71° W), Lisbon (38.71° N, 9.13° W), Paris (48.85° N, 2.35° E), Rome (41.9° N, 12.50° E), and Rotterdam (51.91° N, 4.46° E). We show that OMI NO2 tropospheric column data can be used to assess the evolution of NO2 over important European cities. According to the statistical analysis, using the seasonal variation, we found good correlations (R > 0.50) between OMI and ground-based in-situ observations for all of the cities presented in this work. Highest correlation coefficients (R > 0.80) between ground-based monitoring stations and OMI observations were calculated for the cities of Berlin, Madrid, and Rome. Both types of observations, in-situ and remote sensing, show an NO2 negative trend for all of locations presented in this study

Testing Different Interpolation Methods Based on Single Beam Echosounder River Surveying. Case Study: Siret River

Bathymetric measurements play an important role in assessing the sedimentation rate, deposition of pollutants, erosion rate, or monitoring of morphological changes in a river, lake, or accumulation basin. In order to create a coherent and continuous digital elevation model (DEM) of a river bed, various data interpolation methods are used, especially when single-beam bathymetric measurements do not cover the entire area and when there are areas which are not measured. Interpolation methods are based on numerical models applied to natural landscapes (e.g., meandering river) by taking into account various morphometric and morphologies and a wide range of scales. Obviously, each interpolation method, used in standard or customised form, yields different results. This study aims at testing four interpolation methods in order to determine the most appropriate method which will give an accurate description of the riverbed, based on single-beam bathymetric measurements. The four interpolation methods selected in the present research are: inverse distance weighting (IDW), radial basis function (RBF) with completely regularized spline (CRS) which uses deterministic interpolation, simple kriging (KRG) which is a geo-statistical method, and Topo to Raster (TopoR), a particular method specifically designed for creating continuous surfaces from various elevation points, contour, or polygon data, suitable for creating surfaces for hydrologic analysis. Digital elevation models (DEM’s) were statistically analyzed and precision and errors were evaluated. The single-beam bathymetric measurements were made on the Siret River, between 0 and 35 km. To check and validate the methods, the experiment was repeated for five randomly selected cross-sections in a 1500 m section of the river. The results were then compared with the data extracted from each elevation model generated with each of the four interpolation methods. Our results show that: 1) TopoR is the most accurate technique, and 2) the two deterministic methods give large errors in bank areas, for the entire river channel and for the particular cross-sections